




import argparse
import json
import os
from pathlib import Path

import requests
import torch

from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_						 =					logging.get_logger(__name__)


lowerCAmelCase_						 =					'https://openaipublic.azureedge.net/jukebox/models/'
lowerCAmelCase_						 =					{
    'jukebox-1b-lyrics': [
        '5b/vqvae.pth.tar',
        '5b/prior_level_0.pth.tar',
        '5b/prior_level_1.pth.tar',
        '1b_lyrics/prior_level_2.pth.tar',
    ],
    'jukebox-5b-lyrics': [
        '5b/vqvae.pth.tar',
        '5b/prior_level_0.pth.tar',
        '5b/prior_level_1.pth.tar',
        '5b_lyrics/prior_level_2.pth.tar',
    ],
}






def    snake_case(							__magic_name__       )					->				Optional[Any]:





     '''simple docstring'''


     if key.endswith('''.model.1.bias'''       ) and len(key.split('''.'''       )       ) > 10:
          lowercase   :  Optional[int]        =					key.replace('''.model.1.bias'''					,						'''.conv1d_1.bias'''       )
     elif key.endswith('''.model.1.weight'''       ) and len(key.split('''.'''       )       ) > 10:
          lowercase   :  int        =					key.replace('''.model.1.weight'''					,						'''.conv1d_1.weight'''       )
     elif key.endswith('''.model.3.bias'''       ) and len(key.split('''.'''       )       ) > 10:
          lowercase   :  List[Any]        =					key.replace('''.model.3.bias'''					,						'''.conv1d_2.bias'''       )
     elif key.endswith('''.model.3.weight'''       ) and len(key.split('''.'''       )       ) > 10:
          lowercase   :  Union[str, Any]        =					key.replace('''.model.3.weight'''					,						'''.conv1d_2.weight'''       )

     if "conditioner_blocks.0." in key:
          lowercase   :  int        =					key.replace('''conditioner_blocks.0'''					,						'''conditioner_blocks'''       )

     if "prime_prior" in key:
          lowercase   :  Union[str, Any]        =					key.replace('''prime_prior'''					,						'''encoder'''       )

     if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
          lowercase   :  Union[str, Any]        =					key.replace('''.emb.'''					,						'''.'''       )

     if key.endswith('''k'''       ):  # replace vqvae.X.k with vqvae.X.codebook
          return key.replace('''.k'''					,						'''.codebook'''       )
     if "y_emb." in key:
          return key.replace('''y_emb.'''					,						'''metadata_embedding.'''       )

     if "x_emb.emb." in key:
          lowercase   :  Optional[Any]        =					key.replace('''0.x_emb.emb'''					,						'''embed_tokens'''       )

     if "prime_state_ln" in key:
          return key.replace('''prime_state_ln'''					,						'''encoder.final_layer_norm'''       )
     if ".ln" in key:
          return key.replace('''.ln'''					,						'''.layer_norm'''       )
     if "_ln" in key:
          return key.replace('''_ln'''					,						'''_layer_norm'''       )

     if "prime_state_proj" in key:
          return key.replace('''prime_state_proj'''					,						'''encoder.proj_in'''       )
     if "prime_x_out" in key:
          return key.replace('''prime_x_out'''					,						'''encoder.lm_head'''       )
     if "prior.x_out" in key:
          return key.replace('''x_out'''					,						'''fc_proj_out'''       )
     if "x_emb" in key:
          return key.replace('''x_emb'''					,						'''embed_tokens'''       )

     return key






def    snake_case(							__magic_name__					,						__magic_name__					,						__magic_name__					,						__magic_name__       )					->				Any:





     '''simple docstring'''


     lowercase   :  Optional[Any]        =					{}
     import re

     lowercase   :  List[Any]        =					re.compile(r'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'''       )
     lowercase   :  List[Any]        =					re.compile(
         r'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''       )
     lowercase   :  Optional[int]        =					re.compile(r'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'''       )

     lowercase   :  Optional[int]        =					re.compile(r'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'''       )
     lowercase   :  List[str]        =					re.compile(
         r'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''       )
     lowercase   :  List[str]        =					re.compile(r'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'''       )

     lowercase   :  Tuple        =					re.compile(r'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)'''       )
     lowercase   :  Dict        =					re.compile(
         r'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''       )
     lowercase   :  Any        =					re.compile(r'''conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)'''       )

     for original_key, value in state_dict.items():
          # rename vqvae.encoder keys
          if re_encoder_block_conv_in.fullmatch(_A       ):
               lowercase   :  Optional[Any]        =					re_encoder_block_conv_in.match(_A       )
               lowercase   :  Tuple        =					regex_match.groups()
               lowercase   :  Any        =					int(groups[2]       ) * 2 + int(groups[3]       )
               lowercase   :  Optional[Any]        =					F"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"""
               lowercase   :  Any        =					re_encoder_block_conv_in.sub(_A					,						_A       )

          elif re_encoder_block_resnet.fullmatch(_A       ):
               lowercase   :  str        =					re_encoder_block_resnet.match(_A       )
               lowercase   :  Tuple        =					regex_match.groups()
               lowercase   :  Tuple        =					int(groups[2]       ) * 2 + int(groups[3]       )
               lowercase   :  Any        =					{'''1''': 1, '''3''': 2}[groups[-2]]
               lowercase   :  List[Any]        =					F"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}."""
               lowercase   :  Tuple        =					F"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
               lowercase   :  Union[str, Any]        =					prefix + resnet_block
               lowercase   :  Any        =					re_encoder_block_resnet.sub(_A					,						_A       )

          elif re_encoder_block_proj_out.fullmatch(_A       ):
               lowercase   :  Any        =					re_encoder_block_proj_out.match(_A       )
               lowercase   :  List[Any]        =					regex_match.groups()
               lowercase   :  int        =					F"""encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"""
               lowercase   :  Optional[int]        =					re_encoder_block_proj_out.sub(_A					,						_A       )

          # rename vqvae.decoder keys
          elif re_decoder_block_conv_out.fullmatch(_A       ):
               lowercase   :  List[str]        =					re_decoder_block_conv_out.match(_A       )
               lowercase   :  Tuple        =					regex_match.groups()
               lowercase   :  Optional[int]        =					int(groups[2]       ) * 2 + int(groups[3]       ) - 2
               lowercase   :  List[str]        =					F"""decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}"""
               lowercase   :  Dict        =					re_decoder_block_conv_out.sub(_A					,						_A       )

          elif re_decoder_block_resnet.fullmatch(_A       ):
               lowercase   :  Tuple        =					re_decoder_block_resnet.match(_A       )
               lowercase   :  Union[str, Any]        =					regex_match.groups()
               lowercase   :  int        =					int(groups[2]       ) * 2 + int(groups[3]       ) - 2
               lowercase   :  Tuple        =					{'''1''': 1, '''3''': 2}[groups[-2]]
               lowercase   :  List[str]        =					F"""decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}."""
               lowercase   :  List[Any]        =					F"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
               lowercase   :  str        =					prefix + resnet_block
               lowercase   :  Optional[Any]        =					re_decoder_block_resnet.sub(_A					,						_A       )

          elif re_decoder_block_proj_in.fullmatch(_A       ):
               lowercase   :  List[Any]        =					re_decoder_block_proj_in.match(_A       )
               lowercase   :  List[Any]        =					regex_match.groups()
               lowercase   :  Union[str, Any]        =					F"""decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"""
               lowercase   :  Optional[Any]        =					re_decoder_block_proj_in.sub(_A					,						_A       )

          # rename prior cond.model to upsampler.upsample_block and resnet
          elif re_prior_cond_conv_out.fullmatch(_A       ):
               lowercase   :  str        =					re_prior_cond_conv_out.match(_A       )
               lowercase   :  Tuple        =					regex_match.groups()
               lowercase   :  List[str]        =					int(groups[1]       ) * 2 + int(groups[2]       ) - 2
               lowercase   :  List[str]        =					F"""conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"""
               lowercase   :  Union[str, Any]        =					re_prior_cond_conv_out.sub(_A					,						_A       )

          elif re_prior_cond_resnet.fullmatch(_A       ):
               lowercase   :  Union[str, Any]        =					re_prior_cond_resnet.match(_A       )
               lowercase   :  Optional[Any]        =					regex_match.groups()
               lowercase   :  str        =					int(groups[1]       ) * 2 + int(groups[2]       ) - 2
               lowercase   :  Optional[int]        =					{'''1''': 1, '''3''': 2}[groups[-2]]
               lowercase   :  List[str]        =					F"""conditioner_blocks.upsampler.upsample_block.{block_index}."""
               lowercase   :  List[Any]        =					F"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
               lowercase   :  int        =					prefix + resnet_block
               lowercase   :  List[Any]        =					re_prior_cond_resnet.sub(_A					,						_A       )

          elif re_prior_cond_proj_in.fullmatch(_A       ):
               lowercase   :  int        =					re_prior_cond_proj_in.match(_A       )
               lowercase   :  Union[str, Any]        =					regex_match.groups()
               lowercase   :  Dict        =					F"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
               lowercase   :  List[str]        =					re_prior_cond_proj_in.sub(_A					,						_A       )

          # keep original key
          else:
               lowercase   :  Union[str, Any]        =					original_key

          lowercase   :  int        =					replace_key(_A       )

          if F"""{key_prefix}.{key}""" not in model_state_dict or key is None:
               print(F"""failed converting {original_key} to {key}, does not match"""       )

          # handle missmatched shape
          elif value.shape != model_state_dict[F"""{key_prefix}.{key}"""].shape:
               lowercase   :  List[str]        =					model_state_dict[F"""{key_prefix}.{key}"""]
               print(F"""{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match"""       )
               lowercase   :  Optional[Any]        =					original_key

          lowercase   :  Union[str, Any]        =					original_key
          lowercase   :  Optional[int]        =					value

     return new_dict






@torch.no_grad()
def    snake_case(							__magic_name__=None					,						__magic_name__=None       )					->				Dict:





     '''simple docstring'''


     for file in MODEL_MAPPING[model_name]:
          if not os.path.isfile(F"""{pytorch_dump_folder_path}/{file.split('/'       )[-1]}"""       ):
               lowercase   :  Optional[int]        =					requests.get(F"""{PREFIX}{file}"""					,						allow_redirects=_A       )
               os.makedirs(F"""{pytorch_dump_folder_path}/"""					,						exist_ok=_A       )
               open(F"""{pytorch_dump_folder_path}/{file.split('/'       )[-1]}"""					,						'''wb'''       ).write(r.content       )

     lowercase   :  Dict        =					MODEL_MAPPING[model_name.split('''/'''       )[-1]]

     lowercase   :  Union[str, Any]        =					JukeboxConfig.from_pretrained(_A       )
     lowercase   :  List[Any]        =					JukeboxModel(_A       )

     lowercase   :  List[str]        =					[]
     lowercase   :  Optional[Any]        =					{}
     for i, dict_name in enumerate(_A       ):
          lowercase   :  Union[str, Any]        =					torch.load(F"""{pytorch_dump_folder_path}/{dict_name.split('/'       )[-1]}"""       )['''model''']

          lowercase   :  List[Any]        =					{}
          for k in old_dic.keys():
               if k.endswith('''.b'''       ):
                    lowercase   :  Optional[int]        =					old_dic[k]
               elif k.endswith('''.w'''       ):
                    lowercase   :  Tuple        =					old_dic[k]
               elif "level_2" not in dict_name and "cond.model." in k:
                    lowercase   :  int        =					old_dic[k]
               else:
                    lowercase   :  Optional[Any]        =					old_dic[k]

          lowercase   :  str        =					'''vqvae''' if i == 0 else F"""priors.{3 - i}"""
          lowercase   :  str        =					fix_jukebox_keys(_A					,						model.state_dict()					,						_A					,						_A       )
          weight_dict.append(_A       )

     lowercase   :  Tuple        =					weight_dict.pop(0       )
     model.vqvae.load_state_dict(_A       )
     for i in range(len(_A       )       ):
          model.priors[i].load_state_dict(weight_dict[2 - i]       )

     Path(_A       ).mkdir(exist_ok=_A       )
     with open(F"""{pytorch_dump_folder_path}/mapping.json"""					,						'''w'''       ) as txtfile:
          json.dump(_A					,						_A       )

     print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""       )
     model.save_pretrained(_A       )

     return weight_dict


if __name__ == "__main__":
 lowerCAmelCase_						 =					argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--model_name',
     default='jukebox-5b-lyrics',
     type=str,
     help='Name of the model you\'d like to convert.',
 )
 parser.add_argument(
     '--pytorch_dump_folder_path',
     default='jukebox-5b-lyrics-converted',
     type=str,
     help='Path to the output PyTorch model directory.',
 )
 lowerCAmelCase_						 =					parser.parse_args()
 convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)



def 							UpperCamelCase_(					_A :Union[str, Any]	)->		List[str]:
			UpperCamelCase__       =							[0] * len(_A	)
			UpperCamelCase__       =							[]
			UpperCamelCase__       =							[]
			UpperCamelCase__       =							0

			for values in graph.values():
						for i in values:
									indegree[i] += 1

			for i in range(len(_A	)	):
						if indegree[i] == 0:
									queue.append(_A	)

			while queue:
						UpperCamelCase__       =							queue.pop(0	)
						cnt += 1
						topo.append(_A	)
						for x in graph[vertex]:
									indegree[x] -= 1
									if indegree[x] == 0:
												queue.append(_A	)

			if cnt != len(_A	):
						print("Cycle exists"	)
			else:
						print(_A	)


# Adjacency List of Graph
__UpperCamelCase     		=							{0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}







topological_sort(graph)


